{"id":"W1996528179","doi":"10.3390/rs5094533","title":"A Study of Soil Line Simulation from Landsat Images in Mixed Grassland","year":2013,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; University of Saskatchewan","keywords":"Environmental science; Grassland; Canopy; Vegetation (pathology); Soil science; Remote sensing; Hydrology (agriculture); Geology; Agronomy; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000127336,0.0001442833,0.0002221781,0.00004338929,0.00004716916,0.00003508593,0.00007684766,0.00008087386,0.00004535109],"category_scores_gemma":[0.00009235837,0.0001117309,0.00003475172,0.0002789974,0.00004671765,0.0001462296,0.00009401057,0.0001478708,0.0001211544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008813735,"about_ca_system_score_gemma":0.000003228017,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01878666,"about_ca_topic_score_gemma":0.005386564,"domain_scores_codex":[0.9987893,0.0001286535,0.0002922565,0.000296275,0.0002944521,0.0001991114],"domain_scores_gemma":[0.9993991,0.0001483155,0.000125074,0.0002560503,0.00002081994,0.00005060035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002191238,0.0001548863,0.02493491,0.000006368124,0.00001948112,0.00005408271,0.003086766,0.4856853,0.3017509,6.105014e-8,0.0004380845,0.1838472],"study_design_scores_gemma":[0.0006606138,0.00006251771,0.4150234,0.00004961316,0.00001409137,0.000005257887,0.0007458895,0.5771932,0.005790734,0.0002694099,0.000032354,0.0001529009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970735,0.00001247091,0.0009136323,0.0001098851,0.0001068164,0.0003402111,0.000001100974,0.00003473386,0.001407596],"genre_scores_gemma":[0.9934616,0.000003700715,0.006311099,0.00002563683,0.00005992017,5.498122e-9,0.000008237059,0.00001391138,0.0001158764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3900885,"threshold_uncertainty_score":0.9877473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01254746747927477,"score_gpt":0.2339782723016364,"score_spread":0.2214308048223616,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}